Error in WinBUGS CODE - r

Question

I could not get the WinBUGS code below to work. It works for normal priors, but not for uniform priors. The error message that appears after I click compile is array index is greater than array upper bound for age. What does that mean? Can any one please help me work the code below please?
model
{
for (i in 1:n) {
# Linear regression on logit
logit(p[i]) <- alpha + b.sex*sex[i] + b.age*age[i]
# Likelihood function for each data point
frac[i] ~ dbern(p[i])
}
alpha ~ dunif(0, 1) # Prior for intercept
b.sex ~ dunif(0, 1) # Prior for slope of sex
b.age ~ dunif(0, 1) # Prior for slope of age
}
Data
list(sex=c(1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1,
1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0,
0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,
0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1),
age= c(69, 57, 61, 60, 69, 74, 63, 68, 64, 53, 60, 58, 79, 56, 53, 74, 56, 76, 72,
56, 66, 52, 77, 70, 69, 76, 72, 53, 69, 59, 73, 77, 55, 77, 68, 62, 56, 68, 70, 60,
57, 51, 51, 63, 57, 80, 52, 65, 72, 80, 73, 76, 79, 66, 51, 76, 75, 66, 75, 78, 70,
67, 51, 70, 71, 71, 74, 74, 60, 58, 55, 61, 65, 52, 68, 75, 52, 53, 70),
frac=c(1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,
1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1,
1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 0, 0, 1, 0, 0, 1),
n=100)
Initial Values
list(alpha=0.5, b.sex=0.5, b.age=0.5)

Answer 1

Oh, that's clear. WinBUGS says array index is greater than array upper bound for age.
That clearly hints an error -> I see you have n = 100 and the age list is not long enough:
> your_list <- list(...)
> str(your_list)
List of 4
$ sex : num [1:100] 1 1 1 0 1 1 0 0 0 0 ...
$ age : num [1:79] 69 57 61 60 69 74 63 68 64 53 ...
$ frac: num [1:100] 1 1 1 0 1 1 0 1 1 0 ...
$ n : num 100
Anyways, I wouldn't use uniform prior here; unless you actually know what you are doing, I would recommend flat normal, like dnorm(0, 1.0E-10) or so. You should also allow negative values for the coefficients. The "null hypothesis" normally is that the coefficient is zero, so for the mean value of the posterior distribution of the coefficient to be zero, you should "allow it some space from both sides" (intuitivelly said).